Object categorization using hierarchical wavelet packet texture descriptors

Object categorization plays an important role in computer vision, semantic based image content understanding, and image retrieval. Wavelet packet transform provides a very good observation for the images by sub-band filtering. Different objects have distinctive characteristics in the sub-bands of wavelet packets, which should be discriminative for objects classification. In this paper, an object categorization method using hierarchical wavelet packet texture descriptors is proposed. Comparisons between Gabor texture descriptor, pyramid of histograms of orientation gradients (PHOG) and the proposed hierarchical wavelet packet texture descriptors on the widely used OT, Scene-13 and Sport event datasets are also given. Experimental results show that object categorization performances of the proposed texture descriptors are better than that of Gabor texture descriptor and as good as that of PHOG shape descriptor. Object categorization performances of the texture descriptors under various decomposition levels and wavelet bases are discussed. Performances of texture descriptors of global and local images with different partition patterns are also analyzed.